{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33b85008",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/6_decision_trees_for_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75eed28",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5ba4fe",
   "metadata": {},
   "source": [
    "<a name='0'></a>\n",
    "# Decision Trees - Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07494ffb",
   "metadata": {},
   "source": [
    "Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets. \n",
    "\n",
    "In the previous notebook, we learnt to predict the continous values with decision trees (regression). This notebook is for classification. \n",
    "\n",
    "Different to other algorithms, here are two things that trees are very particular about:\n",
    "\n",
    "Here are 7 interesting facts about decision trees:\n",
    "\n",
    "* They do not need the numerical input data to be scaled. Whatever the numerical values are, decision trees don't care. \n",
    "\n",
    "* Decision trees handle categorical features in the raw text format (Scikit-Learn doesn't support this, TensorFlow's trees implementation does).\n",
    "\n",
    "* Different to other complex learning algorithms, the results of decision trees can be interpreted. It's fair to say that decision trees are not blackbox type models. \n",
    "* While most models will suffer from missing values, decision trees are okay with them.\n",
    "* Trees can handle imbalanced datasets. You will only have to adjust the weights of the classes.\n",
    "* Trees can provide the feature importances or how much each feature contributed to the model training results.\n",
    "* Trees are the basic building blocks of ensemble methods such as random forests and gradient boosting machines.\n",
    "\n",
    "The way decision trees works is like the series of if/else questions. Let's say that you want to make a decision of the car to buy. In order to get the right car to buy, you could go on and evaluate the level of the safety, the number of sits and doors by asking series of if like questions. \n",
    "\n",
    "Here is the structure of the decision trees.\n",
    "\n",
    "![Decision Trees.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1632212349927/4XijLSXib.png)\n",
    "\n",
    "\n",
    "A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as `max_depth`hyperparameter) in decision trees. We will see other techniques to avoid overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e2c5a6",
   "metadata": {},
   "source": [
    "## Decision Trees for Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e8372",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* [1 - Imports](#1)\n",
    "* [2 - Loading the data](#2)\n",
    "* [3 - Exploratory Analysis](#3)\n",
    "* [4 - Preprocessing the data](#4)\n",
    "* [5 - Training Decision Trees](#5)\n",
    "* [6 - Evaluating Decision Trees](#6)\n",
    "* [7 - Improving Decision Trees](#7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99058d12",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "df278c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35699c38",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "\n",
    "## 2 - Loading the data\n",
    "\n",
    "In this classification task with decision trees, we will use a car dataset that is avilable at [OpenML](https://www.openml.org/d/21) to predict the car acceptability given the information about the car. We will load it with Sklearn `fetch_openml` function. \n",
    "\n",
    "The version of the data we are fetching is 2. In the version 1 of the dataset, the target class had 4 classes (unacc, acc, good, vgood) but in the second version, the majority class is Positive(P) whereas the rest are Negative(P). If you want to see version 1, you can change version parameter in a cell below. \n",
    "\n",
    "\n",
    "Here are the informations about the features:\n",
    "\n",
    "* *buying*: The buying price of the car(vhigh, high, med, low)\n",
    "* maint: The maintainance price of the car(high, high, med, low)\n",
    "* *doors*: The number of doors (2,3,4,5more)\n",
    "* *persons*: The number of persons that can be carried the car. They are 2, 4, and more. \n",
    "* *lug_boot*: The size of the luggage boot (small, med, big)\n",
    "* *safety*: Estimated safefy of the car(low, med, high)\n",
    "* *BinaryClass(target feature)*: The car acceptability class. Either positive(P) or negative(N).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "d3083946",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "car_data = fetch_openml(name='car', version=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "46c09852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(car_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "154ed67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '991',\n",
       " 'name': 'car',\n",
       " 'version': '2',\n",
       " 'description_version': '1',\n",
       " 'format': 'ARFF',\n",
       " 'upload_date': '2014-10-04T22:44:31',\n",
       " 'licence': 'Public',\n",
       " 'url': 'https://www.openml.org/data/v1/download/53525/car.arff',\n",
       " 'file_id': '53525',\n",
       " 'default_target_attribute': 'binaryClass',\n",
       " 'tag': ['derived',\n",
       "  'mythbusting_1',\n",
       "  'study_1',\n",
       "  'study_15',\n",
       "  'study_20',\n",
       "  'study_41',\n",
       "  'study_7'],\n",
       " 'visibility': 'public',\n",
       " 'minio_url': 'http://openml1.win.tue.nl/dataset991/dataset_991.pq',\n",
       " 'status': 'active',\n",
       " 'processing_date': '2020-11-20 20:17:54',\n",
       " 'md5_checksum': '49c57b793eef1b8e55f297e5e019fdbf'}"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "d93da4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2'"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.details['version'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "69e438fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Author**:   \n",
      "**Source**: Unknown - Date unknown  \n",
      "**Please cite**:   \n",
      "\n",
      "Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "# Data description \n",
    "\n",
    "print(car_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "d7180991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety']"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying feature names\n",
    "\n",
    "car_data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "e3ba092e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>binaryClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>med</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>high</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>med</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  buying  maint doors persons lug_boot safety binaryClass\n",
       "0  vhigh  vhigh     2       2    small    low           P\n",
       "1  vhigh  vhigh     2       2    small    med           P\n",
       "2  vhigh  vhigh     2       2    small   high           P\n",
       "3  vhigh  vhigh     2       2      med    low           P\n",
       "4  vhigh  vhigh     2       2      med    med           P"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the whole dataframe\n",
    "\n",
    "car_data = car_data.frame\n",
    "car_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "2f05b049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(car_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c492fd93",
   "metadata": {},
   "source": [
    "<a name='3'></a>\n",
    "## 3 - Exploratory Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9906083",
   "metadata": {},
   "source": [
    "Before doing exploratory analysis, let's get the training and test data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff2de1d",
   "metadata": {},
   "source": [
    "### Splitting Data into Training and Test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "98d74144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training data is: 1555 \n",
      "The size of testing data is: 173\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data = train_test_split(car_data, test_size=0.1,random_state=20)\n",
    "\n",
    "print('The size of training data is: {} \\nThe size of testing data is: {}'.format(len(train_data), len(test_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1127e7",
   "metadata": {},
   "source": [
    "### Checking Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "753bb539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>binaryClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "      <td>1555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>402</td>\n",
       "      <td>392</td>\n",
       "      <td>393</td>\n",
       "      <td>521</td>\n",
       "      <td>522</td>\n",
       "      <td>528</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buying maint doors persons lug_boot safety binaryClass\n",
       "count    1555  1555  1555    1555     1555   1555        1555\n",
       "unique      4     4     4       3        3      3           2\n",
       "top       med   low     2       2      med    low           P\n",
       "freq      402   392   393     521      522    528        1097"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking summary statistics\n",
    "\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6e747c",
   "metadata": {},
   "source": [
    "### Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "f0e4fb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying         0\n",
       "maint          0\n",
       "doors          0\n",
       "persons        0\n",
       "lug_boot       0\n",
       "safety         0\n",
       "binaryClass    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing values\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4977a1a1",
   "metadata": {},
   "source": [
    "We don't have any missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da8fe66",
   "metadata": {},
   "source": [
    "### Checking Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff90081a",
   "metadata": {},
   "source": [
    "Let's inspect some categorical features that are in the dataset, almost all re. Let's see that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "7c5e465c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "med      402\n",
       "vhigh    387\n",
       "high     385\n",
       "low      381\n",
       "Name: buying, dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['buying'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "d88aefd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='buying', ylabel='count'>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAJNCAYAAACImWznAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgj0lEQVR4nO3debSnB13f8c+XTFgUFDBXDFk6iKkYF4KOKRZUBJSl1qAixI0UaaOCC0dRwR4LWunBo0hdsVGQoJRVI6nFBVkVC3ECMWQBmUJoMickIzulxCZ8+8d9ItfpJLmTmd+9M995vc655z7r737vOfwY3jzP77nV3QEAAGCWO2z3AAAAABx+Yg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAbasd0DHIoTTjihd+7cud1jAAAAbIuLL77477t77UD7jurY27lzZ3bv3r3dYwAAAGyLqnr/Le1zGycAAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADrTz2quq4qnpHVf3xsn6fqnpbVe2pqpdX1R2X7Xda1vcs+3euejYAAICpdmzBz/jRJFcm+Zxl/ReSPK+7X1ZVv5XkSUmev3z/cHd/UVWdvRz3+C2YDwCOKA/6tQdt9whwQG/54bds9wjAQVjplb2qOjnJv0ryO8t6JXloklcth5yf5DHL8lnLepb9D1uOBwAA4CCt+jbO/5zkJ5N8eln/vCQf6e4bl/Vrkpy0LJ+U5OokWfZ/dDkeAACAg7Sy2Kuqb05yfXdffJhf99yq2l1Vu/ft23c4XxoAAGCMVV7Ze1CSb6mqq5K8LOu3b/5KkrtX1c2fFTw5yd5leW+SU5Jk2f+5ST64/4t293ndvau7d62tra1wfAAAgKPXymKvu5/R3Sd3984kZyd5fXd/d5I3JHnsctg5SV69LF+4rGfZ//ru7lXNBwAAMNl2/J29n0ryY1W1J+ufyXvBsv0FST5v2f5jSZ6+DbMBAACMsBV/eiHd/cYkb1yW35vkzAMc86kk37EV8wAAAEy3HVf2AAAAWDGxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYaMd2D3Ck+aqfePF2jwC36OJffMJ2jwAAwFHClT0AAICBxB4AAMBAbuMEDqv/9XNfvt0jwC069T+8c7tHAIAt48oeAADAQGIPAABgILEHAAAwkNgDAAAYyANaAAAY5U1f9/XbPQIc0Ne/+U1b+vNc2QMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABlpZ7FXVnavqoqr626q6vKp+dtn+oqp6X1VdsnydsWyvqvrVqtpTVZdW1VeuajYAAIDpdqzwtW9I8tDu/kRVHZ/kr6rqT5Z9P9Hdr9rv+EclOW35+hdJnr98BwAA4CCt7Mper/vEsnr88tW3cspZSV68nPfWJHevqhNXNR8AAMBkK/3MXlUdV1WXJLk+yWu7+23Lrmcvt2o+r6rutGw7KcnVG06/ZtkGAADAQVpp7HX3Td19RpKTk5xZVV+W5BlJ7pfkq5PcM8lPHcxrVtW5VbW7qnbv27fvcI8MAAAwwpY8jbO7P5LkDUke2d3XLrdq3pDkd5OcuRy2N8kpG047edm2/2ud1927unvX2traiicHAAA4Oq3yaZxrVXX3ZfkuSb4xybtu/hxeVVWSxyS5bDnlwiRPWJ7K+cAkH+3ua1c1HwAAwGSrfBrniUnOr6rjsh6Vr+juP66q11fVWpJKckmSH1iOf02SRyfZk+STSZ64wtkAAABGW1nsdfelSR5wgO0PvYXjO8lTVjUPAADAsWRLPrMHAADA1hJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYaGWxV1V3rqqLqupvq+ryqvrZZft9quptVbWnql5eVXdctt9pWd+z7N+5qtkAAACmW+WVvRuSPLS775/kjCSPrKoHJvmFJM/r7i9K8uEkT1qOf1KSDy/bn7ccBwAAwO2wstjrdZ9YVo9fvjrJQ5O8atl+fpLHLMtnLetZ9j+sqmpV8wEAAEy20s/sVdVxVXVJkuuTvDbJ/0zyke6+cTnkmiQnLcsnJbk6SZb9H03yeaucDwAAYKqVxl5339TdZyQ5OcmZSe53qK9ZVedW1e6q2r1v375DfTkAAICRtuRpnN39kSRvSPI1Se5eVTuWXScn2bss701ySpIs+z83yQcP8Frndfeu7t61tra26tEBAACOSqt8GudaVd19Wb5Lkm9McmXWo++xy2HnJHn1snzhsp5l/+u7u1c1HwAAwGQ7bvuQ2+3EJOdX1XFZj8pXdPcfV9UVSV5WVT+f5B1JXrAc/4Ikv1dVe5J8KMnZK5wNAABgtJXFXndfmuQBB9j+3qx/fm//7Z9K8h2rmgcAAOBYsiWf2QMAAGBriT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAZaWexV1SlV9YaquqKqLq+qH122P6uq9lbVJcvXozec84yq2lNV766qR6xqNgAAgOl2rPC1b0zy49399qq6W5KLq+q1y77ndfcvbTy4qk5PcnaSL01y7yR/UVX/vLtvWuGMAAAAI63syl53X9vdb1+WP57kyiQn3copZyV5WXff0N3vS7InyZmrmg8AAGCyLfnMXlXtTPKAJG9bNv1QVV1aVS+sqnss205KcvWG067JrcchAAAAt2DlsVdVd03yB0me2t0fS/L8JPdNckaSa5M89yBf79yq2l1Vu/ft23e4xwUAABhhpbFXVcdnPfRe0t1/mCTdfV1339Tdn07y2/nMrZp7k5yy4fSTl23/RHef1927unvX2traKscHAAA4aq3yaZyV5AVJruzuX96w/cQNh31rksuW5QuTnF1Vd6qq+yQ5LclFq5oPAABgslU+jfNBSb43yTur6pJl208n+c6qOiNJJ7kqyfcnSXdfXlWvSHJF1p/k+RRP4gQAALh9VhZ73f1XSeoAu15zK+c8O8mzVzUTAADAsWJLnsYJAADA1hJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAG2lTsVdXrNrMNAACAI8OOW9tZVXdO8llJTqiqeySpZdfnJDlpxbMBAABwO91q7CX5/iRPTXLvJBfnM7H3sSS/vrqxAAAAOBS3Gnvd/StJfqWqfri7f22LZgIAAOAQ3daVvSRJd/9aVf3LJDs3ntPdL17RXAAAAByCTcVeVf1ekvsmuSTJTcvmTiL2AAAAjkCbir0ku5Kc3t29ymEAAAA4PDb7d/YuS/IFqxwEAACAw2ezV/ZOSHJFVV2U5IabN3b3t6xkKgAAAA7JZmPvWascAgAAgMNrs0/jfNOqBwEAAODw2ezTOD+e9advJskdkxyf5H939+esajAAAABuv81e2bvbzctVVUnOSvLAVQ0FAADAodns0zj/Ua/7oySPOPzjAAAAcDhs9jbOb9uweoes/929T61kIgAAAA7ZZp/G+a83LN+Y5Kqs38oJAADAEWizn9l74qoHAQAA4PDZ1Gf2qurkqrqgqq5fvv6gqk5e9XAAAADcPpt9QMvvJrkwyb2Xr/+2bAMAAOAItNnYW+vu3+3uG5evFyVZW+FcAAAAHILNxt4Hq+p7quq45et7knxwlYMBAABw+2029r4vyeOSfCDJtUkem+TfrGgmAAAADtFmY+/nkpzT3Wvd/flZj7+fvbUTquqUqnpDVV1RVZdX1Y8u2+9ZVa+tqvcs3++xbK+q+tWq2lNVl1bVVx7KLwYAAHAs22zsfUV3f/jmle7+UJIH3MY5Nyb58e4+PckDkzylqk5P8vQkr+vu05K8bllPkkclOW35OjfJ8zf9WwAAAPBPbDb27nDzFbhk/epcbuNv9HX3td399mX540muTHJS1v8Y+/nLYecnecyyfFaSF/e6tya5e1WduNlfBAAAgM/Y1B9VT/LcJP+jql65rH9Hkmdv9odU1c6sXwl8W5J7dfe1y64PJLnXsnxSkqs3nHbNsu3aAAAAcFA2FXvd/eKq2p3kocumb+vuKzZzblXdNckfJHlqd3+sqja+bldVH8zAVXVu1m/zzKmnnnowpwIAABwzNntlL0vcbSrwblZVx2c99F7S3X+4bL6uqk7s7muX2zSvX7bvTXLKhtNPXrbtP8d5Sc5Lkl27dh1UKAIAABwrNvuZvYNW65fwXpDkyu7+5Q27LkxyzrJ8TpJXb9j+hOWpnA9M8tENt3sCAABwEDZ9Ze92eFCS703yzqq6ZNn200mek+QVVfWkJO/P+t/vS5LXJHl0kj1JPpnkiSucDQAAYLSVxV53/1WSuoXdDzvA8Z3kKauaBwAA4Fiysts4AQAA2D5iDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgVYWe1X1wqq6vqou27DtWVW1t6ouWb4evWHfM6pqT1W9u6oesaq5AAAAjgWrvLL3oiSPPMD253X3GcvXa5Kkqk5PcnaSL13O+c2qOm6FswEAAIy2stjr7jcn+dAmDz8rycu6+4bufl+SPUnOXNVsAAAA023HZ/Z+qKouXW7zvMey7aQkV2845pplGwAAALfDVsfe85PcN8kZSa5N8tyDfYGqOreqdlfV7n379h3m8QAAAGbY0tjr7uu6+6bu/nSS385nbtXcm+SUDYeevGw70Guc1927unvX2traagcGAAA4Sm1p7FXViRtWvzXJzU/qvDDJ2VV1p6q6T5LTkly0lbMBAABMsmNVL1xVL03ykCQnVNU1SZ6Z5CFVdUaSTnJVku9Pku6+vKpekeSKJDcmeUp337Sq2QAAAKZbWex193ceYPMLbuX4Zyd59qrmAQAAOJZsx9M4AQAAWDGxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgVYWe1X1wqq6vqou27DtnlX12qp6z/L9Hsv2qqpfrao9VXVpVX3lquYCAAA4Fqzyyt6Lkjxyv21PT/K67j4tyeuW9SR5VJLTlq9zkzx/hXMBAACMt7LY6+43J/nQfpvPSnL+snx+ksds2P7iXvfWJHevqhNXNRsAAMB0W/2ZvXt197XL8geS3GtZPinJ1RuOu2bZBgAAwO2wbQ9o6e5O0gd7XlWdW1W7q2r3vn37VjAZAADA0W+rY++6m2/PXL5fv2zfm+SUDcedvGz7/3T3ed29q7t3ra2trXRYAACAo9VWx96FSc5Zls9J8uoN25+wPJXzgUk+uuF2TwAAAA7SjlW9cFW9NMlDkpxQVdckeWaS5yR5RVU9Kcn7kzxuOfw1SR6dZE+STyZ54qrmAgAAOBasLPa6+ztvYdfDDnBsJ3nKqmYBAAA41mzbA1oAAABYHbEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBdmzHD62qq5J8PMlNSW7s7l1Vdc8kL0+yM8lVSR7X3R/ejvkAAACOdtt5Ze8buvuM7t61rD89yeu6+7Qkr1vWAQAAuB2OpNs4z0py/rJ8fpLHbN8oAAAAR7ftir1O8udVdXFVnbtsu1d3X7ssfyDJvbZnNAAAgKPftnxmL8mDu3tvVX1+ktdW1bs27uzurqo+0IlLHJ6bJKeeeurqJwUAADgKbcuVve7eu3y/PskFSc5Mcl1VnZgky/frb+Hc87p7V3fvWltb26qRAQAAjipbHntV9dlVdbebl5N8U5LLklyY5JzlsHOSvHqrZwMAAJhiO27jvFeSC6rq5p//X7v7T6vqb5K8oqqelOT9SR63DbMBAACMsOWx193vTXL/A2z/YJKHbfU8AAAAEx1Jf3oBAACAw0TsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAw0BEXe1X1yKp6d1Xtqaqnb/c8AAAAR6MjKvaq6rgkv5HkUUlOT/KdVXX69k4FAABw9DmiYi/JmUn2dPd7u/sfkrwsyVnbPBMAAMBR50iLvZOSXL1h/ZplGwAAAAdhx3YPcLCq6twk5y6rn6iqd2/nPNymE5L8/XYPMUX90jnbPQJbz3vocHpmbfcEbD3vocOofsR76BjlfXS41EreQ//slnYcabG3N8kpG9ZPXrb9o+4+L8l5WzkUt19V7e7uXds9BxytvIfg0HgPwaHzPjp6HWm3cf5NktOq6j5VdcckZye5cJtnAgAAOOocUVf2uvvGqvqhJH+W5LgkL+zuy7d5LAAAgKPOERV7SdLdr0nymu2eg8PGLbdwaLyH4NB4D8Gh8z46SlV3b/cMAAAAHGZH2mf2AAAAOAzEHrdbVT2kqv74Fvb9TlWdfhvnv6iqHrua6eDIVlU7q+qyA2z/uap6+G2c+6yqetrqpoNjS1W9sao8aZBjVlV9YrtnYDWOuM/sMUN3/9vtngGORt39H7Z7BgBgBlf22JSqek5VPWXD+rOS7Epy16p6VVW9q6peUrX+lyI3/r+kVfWkqvq7qrqoqn67qn59w0t/XVX9dVW911U+jkHHLe+Jy6vqz6vqLhuveFfVo5f31sVV9av7XUk/fXmfvbeqfmSb5odts1wdf9fynvm75d+gh1fVW6rqPVV1ZlV9dlW9cPn35x1VddZy7l2q6mVVdWVVXZDkLtv868ARodb9YlVdVlXvrKrHL9t/o6q+ZVm+oKpeuCx/X1U9eztn5taJPTbr5Uket2H9cUmuS/KAJE9NcnqSL0zyoI0nVdW9k/xMkgcu++633+uemOTBSb45yXNWMDccyU5L8hvd/aVJPpLk22/eUVV3TvJfkjyqu78qydp+594vySOSnJnkmVV1/JZMDEeWL0ry3Ky/H+6X5Luy/m/K05L8dJJ/n+T13X1mkm9I8otV9dlJfjDJJ7v7S5I8M8lXbcPscCT6tiRnJLl/kodn/T1zYpK/TPK1yzEnZf1/92XZ9uYtnpGDIPbYlO5+R5LPr6p7V9X9k3w4ydVJLurua7r700kuSbJzv1PPTPKm7v5Qd//fJK/cb/8fdfenu/uKJPda6S8BR573dfcly/LF+afvn/sleW93v29Zf+l+5/737r6hu/8+yfXx/uHY9L7ufufyb9DlSV7X648Zf2fW30/flOTpVXVJkjcmuXOSU5N8XZLfT5LuvjTJpVs+ORyZHpzkpd19U3dfl+RNSb46S+wtz2O4Isl1SwR+TZK/3rZpuU0+s8fBeGWSxyb5gqxf6UuSGzbsvykH/5+pjefX7R8Njkr7v38O5layQ33vwQQb3wef3rD+6ay/J25K8u3d/e6NJy2fOAA2qbv3VtXdkzwy61fy7pn1u7w+0d0f387ZuHWu7HEwXp7k7KwH3/5X6G7J3yT5+qq6R1XtyIbb1IBb9e4kX1hVO5f1x2/jLHC0+rMkP7zh8+QPWLa/Oeu3fKaqvizJV2zPeHDE+cskj6+q46pqLetXwS9a9r016x/defNy3NOW7xzBxB6b1t2XJ7lbkr3dfe0mz9mb5D9l/b8o3pLkqiQfXdWMMEV3/58kT07yp1V1cZKPx3sHDtZ/THJ8kkur6vJlPUmen/UHjF2Z5Oeyfhs1kFyQ9dua/zbJ65P8ZHd/YNn3l0l2dPeeJG/P+tU9sXeEq/Vb22F1ququ3f2J5creBUle2N0XbPdccKTb8N6pJL+R5D3d/bztngsAODq4ssdWeNby4fjLkrwvyR9t6zRw9Ph3y3vn8iSfm/WncwIAbIorewAAAAO5sgcAADCQ2AMAABhI7AEAAAwk9gA4plXVzqq67DC8zg9U1RMOx0wAcDjs2O4BAGCC7v6t7Z4BADZyZQ8Akh1V9ZKqurKqXlVVn1VVV1XVCUlSVbuq6o1VdYeqek9VrS3b71BVe6pqraqeVVVPW7a/sap+oaouqqq/q6qvXbZ/VlW9oqquqKoLquptVbVr+35tACYTewCQfHGS3+zuL0nysSRPPtBB3f3pJL+f5LuXTQ9P8rfdve8Ah+/o7jOTPDXJM5dtT07y4e4+PcnPJPmqw/YbAMB+xB4AJFd391uW5d9P8uBbOfaFSW7+bN73JfndWzjuD5fvFyfZuSw/OMnLkqS7L0ty6e2cFwBuk9gDgKQPsH5jPvPv5J3/cUf31Umuq6qHJjkzyZ/cwmvesHy/KT4jD8A2EHsAkJxaVV+zLH9Xkr9KclU+c5vlt+93/O9k/QrgK7v7poP4OW9J8rgkqarTk3z57R0YAG6L2AOA5N1JnlJVVya5R5LnJ/nZJL9SVbuzfnVuowuT3DW3fAvnLfnNJGtVdUWSn09yeZKPHsrgAHBLqnv/O1cAgFuzPEHzed39tQd53nFJju/uT1XVfZP8RZIv7u5/WMWcABzbfIYAAA5CVT09yQ/mM0/kPBifleQNVXV8kkryZKEHwKq4sgcAADCQz+wBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGCg/wcn6dOFovGWnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='buying')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "83d9f295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='buying', ylabel='count'>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='buying', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a986c09",
   "metadata": {},
   "source": [
    "As you can see above, the majority class in the buying price is median(`med`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "dc1eadb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low      392\n",
       "med      390\n",
       "high     387\n",
       "vhigh    386\n",
       "Name: maint, dtype: int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['maint'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "433c25f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='maint', ylabel='count'>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='maint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "110b9abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='maint', ylabel='count'>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='maint', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b2c63b",
   "metadata": {},
   "source": [
    "Let's also check what's in doors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "4298a48f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2        393\n",
       "4        393\n",
       "5more    389\n",
       "3        380\n",
       "Name: doors, dtype: int64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['doors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "477a5113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='doors', ylabel='count'>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAJNCAYAAACImWznAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfAElEQVR4nO3de7Ctd13f8c/XJFxEMECOacwJPQymWrQa5BhpUathlItKoiNMmAIRGaMz0cJoVbAdAUemOooRbcVGgyRWhVRISZl4oeFWHLmcQAi5SD1FaJKJ5AjhPsZJ+PaP/US26UmyT85Ze539zes1s2Y/6/c8z9rf88eezDvPWs+q7g4AAACzfMm6BwAAAODIE3sAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADDQsese4HCccMIJvWfPnnWPAQAAsBZXXnnl33b3roPt29Gxt2fPnuzbt2/dYwAAAKxFVX307vZ5GycAAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMNDKY6+qjqmq91fVm5bnj66qd1fV/qp6XVU9YFl/4PJ8/7J/z6pnAwAAmGo7ruy9IMn1m57/UpLzu/urktya5PnL+vOT3Lqsn78cBwAAwH2w0tirqt1JvjvJ7yzPK8kZSf5oOeSiJGct22cuz7Psf9JyPAAAAIdo1Vf2fi3JTyf5wvL8kUk+2d23L89vTHLysn1ykhuSZNn/qeV4AAAADtHKYq+qvifJLd195RF+3XOral9V7Ttw4MCRfGkAAIAxVnll74lJnl5VH0ny2my8ffOVSY6vqmOXY3YnuWnZvinJKUmy7P/yJB+/64t29wXdvbe79+7atWuF4wMAAOxcK4u97n5xd+/u7j1Jzk7ylu7+N0nemuQHlsPOSfLGZfuy5XmW/W/p7l7VfAAAAJOt43v2fibJT1TV/mx8Ju/CZf3CJI9c1n8iyYvWMBsAAMAIx977IYevu9+W5G3L9oeTnH6QY/4uyTO2Yx4AAIDp1nFlDwAAgBUTewAAAAOJPQAAgIHEHgAAwEDbcoOWneTxP3XxukeAu3XlLz933SMA2+CJv/HEdY8AB/XnP/7n6x4BOASu7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHcoAUAgFHe/m3/et0jwEH963e8fVt/nyt7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAx657AGCW//vz/2LdI8DdetTPfXDdIwDAtnFlDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYaGWxV1UPqqr3VNUHquraqnrZsv6aqvrrqrpqeZy2rFdV/XpV7a+qq6vqG1c1GwAAwHTHrvC1b0tyRnd/tqqOS/LOqvrjZd9Pdfcf3eX4pyY5dXl8c5JXLT8BAAA4RCu7stcbPrs8PW559D2ccmaSi5fz3pXk+Ko6aVXzAQAATLbSz+xV1TFVdVWSW5K8ubvfvex6+fJWzfOr6oHL2slJbth0+o3LGgAAAIdopbHX3Xd092lJdic5vaq+LsmLk3xNkm9K8ogkP3Mor1lV51bVvqrad+DAgSM9MgAAwAjbcjfO7v5kkrcmeUp337y8VfO2JL+b5PTlsJuSnLLptN3L2l1f64Lu3tvde3ft2rXiyQEAAHamVd6Nc1dVHb9sPzjJdyb5yzs/h1dVleSsJNcsp1yW5LnLXTmfkORT3X3zquYDAACYbJV34zwpyUVVdUw2ovKS7n5TVb2lqnYlqSRXJfnR5fjLkzwtyf4kn0/yvBXOBgAAMNrKYq+7r07yuIOsn3E3x3eS81Y1DwAAwP3JtnxmDwAAgO0l9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADLSy2KuqB1XVe6rqA1V1bVW9bFl/dFW9u6r2V9XrquoBy/oDl+f7l/17VjUbAADAdKu8sndbkjO6+xuSnJbkKVX1hCS/lOT87v6qJLcmef5y/POT3Lqsn78cBwAAwH2wstjrDZ9dnh63PDrJGUn+aFm/KMlZy/aZy/Ms+59UVbWq+QAAACZb6Wf2quqYqroqyS1J3pzk/yT5ZHffvhxyY5KTl+2Tk9yQJMv+TyV55CrnAwAAmGqlsdfdd3T3aUl2Jzk9ydcc7mtW1blVta+q9h04cOBwXw4AAGCkbbkbZ3d/Mslbk/zLJMdX1bHLrt1Jblq2b0pySpIs+788yccP8loXdPfe7t67a9euVY8OAACwI63ybpy7qur4ZfvBSb4zyfXZiL4fWA47J8kbl+3LludZ9r+lu3tV8wEAAEx27L0fcp+dlOSiqjomG1F5SXe/qaquS/LaqvqFJO9PcuFy/IVJfq+q9if5RJKzVzgbAADAaCuLve6+OsnjDrL+4Wx8fu+u63+X5BmrmgcAAOD+ZFs+swcAAMD2EnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAy0stirqlOq6q1VdV1VXVtVL1jWX1pVN1XVVcvjaZvOeXFV7a+qD1XVk1c1GwAAwHTHrvC1b0/yk939vqp6aJIrq+rNy77zu/tXNh9cVY9NcnaSr03ylUn+Z1X9s+6+Y4UzAgAAjLSyK3vdfXN3v2/Z/kyS65OcfA+nnJnktd19W3f/dZL9SU5f1XwAAACTbctn9qpqT5LHJXn3svRjVXV1Vb26qh6+rJ2c5IZNp92Ye45DAAAA7sbKY6+qvizJ65O8sLs/neRVSR6T5LQkNyd5xSG+3rlVta+q9h04cOBIjwsAADDCSmOvqo7LRuj9fne/IUm6+2PdfUd3fyHJb+eLb9W8Kckpm07fvaz9I919QXfv7e69u3btWuX4AAAAO9Yq78ZZSS5Mcn13/+qm9ZM2HfZ9Sa5Zti9LcnZVPbCqHp3k1CTvWdV8AAAAk63ybpxPTPKcJB+sqquWtZ9N8qyqOi1JJ/lIkh9Jku6+tqouSXJdNu7keZ47cQIAANw3K4u97n5nkjrIrsvv4ZyXJ3n5qmYCAAC4v9iWu3ECAACwvcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIG2FHtVdcVW1gAAADg6HHtPO6vqQUm+NMkJVfXwJLXseliSk1c8GwAAAPfRPcZekh9J8sIkX5nkynwx9j6d5D+tbiwAAAAOxz3GXne/Mskrq+rHu/s3tmkmAAAADtO9XdlLknT3b1TVv0qyZ/M53X3xiuYCAADgMGwp9qrq95I8JslVSe5YljuJ2AMAADgKbSn2kuxN8tju7lUOAwAAwJGx1e/ZuybJP1nlIAAAABw5W72yd0KS66rqPUluu3Oxu5++kqkAAAA4LFuNvZeucggAAACOrK3ejfPtqx4EAACAI2erd+P8TDbuvpkkD0hyXJLPdffDVjUYAAAA991Wr+w99M7tqqokZyZ5wqqGAgAA4PBs9W6c/6A3/PckTz7y4wAAAHAkbPVtnN+/6emXZON79/5uJRMBAABw2LZ6N87v3bR9e5KPZOOtnAAAAByFtvqZvecd6gtX1SlJLk5yYjZu7nJBd7+yqh6R5HVJ9mQjGp/Z3bcunwV8ZZKnJfl8kh/s7vcd6u8FAABgi5/Zq6rdVXVpVd2yPF5fVbvv5bTbk/xkdz82GzdzOa+qHpvkRUmu6O5Tk1yxPE+SpyY5dXmcm+RV9+HfAwAAQLZ+g5bfTXJZkq9cHv9jWbtb3X3znVfmuvszSa5PcnI23v550XLYRUnOWrbPTHLxcgOYdyU5vqpO2vo/BQAAgDttNfZ2dffvdvfty+M1SXZt9ZdU1Z4kj0vy7iQndvfNy66/ycbbPJONELxh02k3LmsAAAAcoq3G3ser6tlVdczyeHaSj2/lxKr6siSvT/LC7v705n3d3fnil7VvSVWdW1X7qmrfgQMHDuVUAACA+42txt4PJXlmNq7E3ZzkB5L84L2dVFXHZSP0fr+737Asf+zOt2cuP29Z1m9Kcsqm03cva/9Id1/Q3Xu7e++uXVu+uAgAAHC/stXY+/kk53T3ru7+imzE38vu6YTl7poXJrm+u391067LkpyzbJ+T5I2b1p9bG56Q5FOb3u4JAADAIdjq9+x9fXffeueT7v5EVT3uXs55YpLnJPlgVV21rP1skl9McklVPT/JR7NxxTBJLs/G1y7sz8ZXLxzy1z0AAACwYaux9yVV9fA7g2/5rrx7PLe735mk7mb3kw5yfCc5b4vzAAAAcA+2GnuvSPIXVfXflufPSPLy1YwEAADA4dpS7HX3xVW1L8kZy9L3d/d1qxsLAACAw7HVK3tZ4k7gAQAA7ABbvRsnAAAAO4jYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgVYWe1X16qq6paqu2bT20qq6qaquWh5P27TvxVW1v6o+VFVPXtVcAAAA9wervLL3miRPOcj6+d192vK4PEmq6rFJzk7ytcs5v1lVx6xwNgAAgNFWFnvd/Y4kn9ji4WcmeW1339bdf51kf5LTVzUbAADAdOv4zN6PVdXVy9s8H76snZzkhk3H3LisAQAAcB9sd+y9KsljkpyW5OYkrzjUF6iqc6tqX1XtO3DgwBEeDwAAYIZtjb3u/lh339HdX0jy2/niWzVvSnLKpkN3L2sHe40Luntvd+/dtWvXagcGAADYobY19qrqpE1Pvy/JnXfqvCzJ2VX1wKp6dJJTk7xnO2cDAACY5NhVvXBV/WGSb09yQlXdmOQlSb69qk5L0kk+kuRHkqS7r62qS5Jcl+T2JOd19x2rmg0AAGC6lcVedz/rIMsX3sPxL0/y8lXNAwAAcH+yjrtxAgAAsGJiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQCuLvap6dVXdUlXXbFp7RFW9uar+avn58GW9qurXq2p/VV1dVd+4qrkAAADuD1Z5Ze81SZ5yl7UXJbmiu09NcsXyPEmemuTU5XFukletcC4AAIDxVhZ73f2OJJ+4y/KZSS5ati9Kctam9Yt7w7uSHF9VJ61qNgAAgOm2+zN7J3b3zcv23yQ5cdk+OckNm467cVkDAADgPljbDVq6u5P0oZ5XVedW1b6q2nfgwIEVTAYAALDzbXfsfezOt2cuP29Z1m9Kcsqm43Yva/+f7r6gu/d2995du3atdFgAAICdartj77Ik5yzb5yR546b15y535XxCkk9tersnAAAAh+jYVb1wVf1hkm9PckJV3ZjkJUl+McklVfX8JB9N8szl8MuTPC3J/iSfT/K8Vc0FAABwf7Cy2OvuZ93Nricd5NhOct6qZgEAALi/WdsNWgAAAFgdsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYKBj1/FLq+ojST6T5I4kt3f33qp6RJLXJdmT5CNJntndt65jPgAAgJ1unVf2vqO7T+vuvcvzFyW5ortPTXLF8hwAAID74Gh6G+eZSS5ati9Kctb6RgEAANjZ1hV7neTPqurKqjp3WTuxu29etv8myYnrGQ0AAGDnW8tn9pJ8S3ffVFVfkeTNVfWXm3d2d1dVH+zEJQ7PTZJHPepRq58UAABgB1rLlb3uvmn5eUuSS5OcnuRjVXVSkiw/b7mbcy/o7r3dvXfXrl3bNTIAAMCOsu2xV1UPqaqH3rmd5LuSXJPksiTnLIedk+SN2z0bAADAFOt4G+eJSS6tqjt//x90959U1XuTXFJVz0/y0STPXMNsAAAAI2x77HX3h5N8w0HWP57kSds9DwAAwERH01cvAAAAcISIPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAABgILEHAAAwkNgDAAAYSOwBAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABhI7AEAAAwk9gAAAAYSewAAAAOJPQAAgIHEHgAAwEBiDwAAYCCxBwAAMJDYAwAAGEjsAQAADCT2AAAABhJ7AAAAAx11sVdVT6mqD1XV/qp60brnAQAA2ImOqtirqmOS/OckT03y2CTPqqrHrncqAACAneeoir0kpyfZ390f7u6/T/LaJGeueSYAAIAd52iLvZOT3LDp+Y3LGgAAAIfg2HUPcKiq6twk5y5PP1tVH1rnPNyrE5L87bqHmKJ+5Zx1j8D28zd0JL2k1j0B28/f0BFU/9bf0P2Uv6MjpVbyN/RP727H0RZ7NyU5ZdPz3cvaP+juC5JcsJ1Dcd9V1b7u3rvuOWCn8jcEh8ffEBw+f0c719H2Ns73Jjm1qh5dVQ9IcnaSy9Y8EwAAwI5zVF3Z6+7bq+rHkvxpkmOSvLq7r13zWAAAADvOURV7SdLdlye5fN1zcMR4yy0cHn9DcHj8DcHh83e0Q1V3r3sGAAAAjrCj7TN7AAAAHAFijyOuqk6pqrdW1XVVdW1VvWDdM8FOU1UPqqr3VNUHlr+jl617JtiJquqYqnp/Vb1p3bPAOlXVR6rqg1V1VVXtW/c8bI+j7jN7jHB7kp/s7vdV1UOTXFlVb+7u69Y9GOwgtyU5o7s/W1XHJXlnVf1xd79r3YPBDvOCJNcnedi6B4GjwHd09xH/vryqqmx8POwLR/q1OTyu7HHEdffN3f2+Zfsz2fiP7MnrnQp2lt7w2eXpccvDh6zhEFTV7iTfneR31j0LHI2q6m1VdX5V7auq66vqm6rqDVX1V1X1C5uO+4mqumZ5vHBZ21NVH6qqi5Nck+SUqvqpqnpvVV3tHSlHB7HHSlXVniSPS/LuNY8CO87y9rOrktyS5M3d7e8IDs2vJfnpJK42wMb/MPyzqrqyqs7dtP73yxem/1aSNyY5L8nXJfnBqnpkVT0+yfOSfHOSJyT54ap63HLuqUl+s7u/NslXL89PT3JaksdX1bdtw7+LeyD2WJmq+rIkr0/ywu7+9LrngZ2mu+/o7tOS7E5yelV93ZpHgh2jqr4nyS3dfeW6Z4GjxLd09zcmeWqS8zaF2GXLzw8muXZ5h9ZtST6c5JQk35Lk0u7+3PKOkzck+dblnI9u+njBdy2P9yd5X5KvyUb8sUY+s8dKLJ8xen2S3+/uN6x7HtjJuvuTVfXWJE/JxltlgHv3xCRPr6qnJXlQkodV1X/t7meveS5Yi+6+afl5S1Vdmo0rcMnGZ8STjSvgt2065Qu591b43KbtSvIfu/u/HIFxOUJc2eOIWz6ke2GS67v7V9c9D+xEVbWrqo5fth+c5DuT/OVah4IdpLtf3N27u3tPkrOTvEXocX9VVQ9ZbpqXqnpINq7AbfV/Hv6vJGdV1Zcu537fsnZXf5rkh5Z3dqWqTq6qrzj86TkcruyxCk9M8pwkH1w+b5QkP9vdl69vJNhxTkpyUVUdk43/MXdJd7t1PAD3xYlJLt34//E5NskfdPefVNWL7u3E5e7qr0nynmXpd7r7/ct9GTYf92dV9c+T/MXyez6b5NnZ+Nw5a1Ldbu4GAAAwjbdxAgAADCT2AAAABhJ7AAAAA4k9AACAgcQeAADAQGIPAA6iql5aVf9u3XMAwH0l9gBgRarK99kCsDZiDwAWVfXvq+p/V9U7k3z1snZaVb2rqq6uqkur6uH3sv62qvq1qtqX5AVV9YyquqaqPlBV71jfvw6A+xuxBwBJqurxSc5OclqSpyX5pmXXxUl+pru/PskHk7zkXtaT5AHdvbe7X5Hk55I8ubu/IcnTV/4PAYCF2AOADd+a5NLu/nx3fzrJZUkekuT47n77csxFSb6tqr78YOubXut1m7b/PMlrquqHkxyz0n8BAGwi9gDgyPvcnRvd/aNJ/kOSU5JcWVWPXNtUANyviD0A2PCOJGdV1YOr6qFJvjcb0XZrVX3rcsxzkry9uz91sPWDvWhVPaa7393dP5fkQDaiDwBWzl3CACBJd7+vql6X5ANJbkny3mXXOUl+q6q+NMmHkzzvXtbv6per6tQkleSK5fUBYOWqu9c9AwAAAEeYt3ECAAAMJPYAAAAGEnsAAAADiT0AAICBxB4AAMBAYg8AAGAgsQcAADCQ2AMAABjo/wHyJqt2ctBjQwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='doors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "f77d35bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='doors', ylabel='count'>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='doors', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9ae2a9",
   "metadata": {},
   "source": [
    "Nothing stunning in the maintenance cost. All cars share the same maintenance costs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "7dc9bb1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       521\n",
       "more    521\n",
       "4       513\n",
       "Name: persons, dtype: int64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['persons'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "0965bc7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='persons', ylabel='count'>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='persons', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "f8fbe08a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "med      522\n",
       "small    517\n",
       "big      516\n",
       "Name: lug_boot, dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['lug_boot'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "d7e678ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lug_boot', ylabel='count'>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='lug_boot', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "b645aa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low     528\n",
       "high    514\n",
       "med     513\n",
       "Name: safety, dtype: int64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['safety'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "0bf059e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='safety', ylabel='count'>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='safety', hue='binaryClass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "f978b550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P    1097\n",
       "N     458\n",
       "Name: binaryClass, dtype: int64"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['binaryClass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "7c7b7048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='binaryClass', ylabel='count'>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=train_data, x='binaryClass')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341e0ea8",
   "metadata": {},
   "source": [
    "As you can see, our data is completely skewed/imbalanced. The positive examples are 2x more than negative examples. \n",
    "\n",
    "So we will remember during the model evaluation that accuracy is not the right metric in this case. Real world datasets comes with their unique blends, dataset can be imbalanced. Missing values can be present. We just have to find the effective way to deal with those issues. So again for evaluation, we will not rely on accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6733727",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "\n",
    "## 4 - Data Preprocessing \n",
    "\n",
    "It is here that we prepare the data to be in the proper format for the machine learning model. \n",
    "\n",
    "### Handling Categorical Features\n",
    "\n",
    "Decision trees don't care if the features are scaled or not, and they can handle the categorical features. There is a [note on documentation](https://scikit-learn.org/stable/modules/tree.html#tree) that the sklearn tree implementation doesn't support categorical features, so I will go ahead and handle them. It's fun anyways :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9517b46f",
   "metadata": {},
   "source": [
    "Before handling categorical features, let's create a training input data and labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "0766b45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_train = train_data.drop('binaryClass', axis=1)\n",
    "car_labels = train_data[['binaryClass']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0768adae",
   "metadata": {},
   "source": [
    "Let's create a pipeline to encode all features in the training input data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "b7ee6d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('ord_enc', OrdinalEncoder())\n",
    "    \n",
    "])\n",
    "\n",
    "car_train_prepared = pipe.fit_transform(car_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc414f79",
   "metadata": {},
   "source": [
    "Let's also handle labels. Labels contain P and N, so we want to convert those into numbers. Here instead of using Ordinary Encoder, we will use Label Encoder. Sklearn is explicity that it is used to encode target features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "b691edf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "\n",
    "label_enc = LabelEncoder()\n",
    "\n",
    "car_labels_prepared = label_enc.fit_transform(car_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd496de6",
   "metadata": {},
   "source": [
    "<a name='5'></a>\n",
    "\n",
    "## 5 - Training Decision Tree Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "8311b87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree_clf = DecisionTreeClassifier()\n",
    "\n",
    "tree_clf.fit(car_train_prepared, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757c0a01",
   "metadata": {},
   "source": [
    "Let's train the same model on the scaled data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "ea28474e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you run this, it will be error. \n",
    "# SKlearn tree implementation doesn't support categorical features\n",
    "\n",
    "#from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#tree_clf = DecisionTreeClassifier()\n",
    "\n",
    "#tree_clf.fit(car_train, car_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afb9bde",
   "metadata": {},
   "source": [
    "<a name='6'></a>\n",
    "\n",
    "## 6 - Evaluating Decision Trees\n",
    "\n",
    "Let's build 3 functions to display accuracy, confusion matrix, and classification report. Classification report contains all useful metrics such as precision, recall, and f1 score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "6962359e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def accuracy(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return accuracy\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    acc = accuracy_score(labels,preds)\n",
    "    \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "5321beb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def conf_matrix(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return confusion matrix\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    cm = confusion_matrix(labels,preds)\n",
    "    \n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "12b6e547",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "def class_report(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return confusion matrix\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    report = classification_report(labels,preds)\n",
    "    report = print(report)\n",
    "    \n",
    "    return report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a052ed6",
   "metadata": {},
   "source": [
    "Let's find the accuracy on the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "a17fed16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(car_train_prepared, tree_clf, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbeda399",
   "metadata": {},
   "source": [
    "Ohh, see! The decision trees overfitted the dataset. Also if we remember well, our data is not balanced. We have many positive examples than negative examples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "b7b71a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 458,    0],\n",
       "       [   0, 1097]])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix(car_train_prepared, tree_clf, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "1e7d0797",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       458\n",
      "           1       1.00      1.00      1.00      1097\n",
      "\n",
      "    accuracy                           1.00      1555\n",
      "   macro avg       1.00      1.00      1.00      1555\n",
      "weighted avg       1.00      1.00      1.00      1555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report(car_train_prepared, tree_clf, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f6022d",
   "metadata": {},
   "source": [
    "The model clearly overfitted the data. Let's see how we can regularize it. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a574a5",
   "metadata": {},
   "source": [
    "<a name='7'></a>\n",
    "\n",
    "## 7 - Improving Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "0f92566d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af110f75",
   "metadata": {},
   "source": [
    "One way to avoid overfitting is reduce number maximum depth of the tree, set by the hyperparameter `max_depth`. Similarly, we can attempt to reduce all hyperparameters with `max`term while also increasing the `min_` term parameters. \n",
    "\n",
    "Also, I set the `class_weight` to `balanced` because our dataset is imbalanced. By setting it to balanced, the model will automatically adjust the class weight based on the number of available samples in all classes. \n",
    "\n",
    "\n",
    "Let's use GridSearch to find best values of these hyperparameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "18f52052",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's hide warnings returned by grid search\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "056818ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5400 candidates, totalling 16200 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=42),\n",
       "             param_grid={'max_depth': [0, 1, 2, 3, 4, 5],\n",
       "                         'max_features': [0, 1, 2, 3, 4, 5],\n",
       "                         'max_leaf_nodes': [0, 1, 2, 3, 4, 5],\n",
       "                         'min_samples_leaf': [0, 1, 2, 3, 4],\n",
       "                         'min_samples_split': [0, 1, 2, 3, 4]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params_grid = {'max_leaf_nodes': list(range(0, 10)), \n",
    "               'min_samples_split': [0,1,2, 3, 4], \n",
    "               'min_samples_leaf': [0,1,2, 3, 4], \n",
    "               'max_depth':[0,1,2,3,4,5],\n",
    "               'max_features':[0,1,2,3,4,5],\n",
    "              'max_leaf_nodes':[0,1,2,3,4,5]}\n",
    "\n",
    "#refit is true by default. The best estimator is trained on the whole dataset \n",
    "\n",
    "grid_search = GridSearchCV(DecisionTreeClassifier(random_state=42, class_weight='balanced'), params_grid, verbose=1, cv=3, refit=True)\n",
    "\n",
    "grid_search.fit(car_train_prepared, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "55a5d7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4,\n",
       " 'max_features': 5,\n",
       " 'max_leaf_nodes': 5,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2}"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "c4a1488d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', max_depth=4, max_features=5,\n",
       "                       max_leaf_nodes=5, random_state=42)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "4995ab2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0631c68d",
   "metadata": {},
   "source": [
    "We can also plot the tree of the improved model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "bb3a6292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety',\n",
       "       'binaryClass'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "bc930bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plot_tree(tree_best, feature_names=car_train.columns, class_names=['N','P']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fc64ef",
   "metadata": {},
   "source": [
    "Let's make prediction on the training data again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "85b1b211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8926045016077171"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(car_train_prepared, tree_best, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "d6e03505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[430,  28],\n",
       "       [139, 958]])"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix(car_train_prepared, tree_best, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd20f6c",
   "metadata": {},
   "source": [
    "In confusion matrix, each row represent an actual class and each column represents predicted class. \n",
    "\n",
    "So, from the results above:\n",
    "\n",
    "* 430 negative examples(N) were correcty predicted as negatives(`true negatives`).\n",
    "* 28 negatives examples(N) were incorrectly classified as positive examples when they are in fact negatives(`false positives`).\n",
    "* 139 positive examples were incorrectly classified as negative(N) when in fact they are positives(P) (`false negatives`).\n",
    "* 958 were correctly classified as positive examples(`true positives`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "bfe10646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.94      0.84       458\n",
      "           1       0.97      0.87      0.92      1097\n",
      "\n",
      "    accuracy                           0.89      1555\n",
      "   macro avg       0.86      0.91      0.88      1555\n",
      "weighted avg       0.91      0.89      0.90      1555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report(car_train_prepared, tree_best, car_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62a574a",
   "metadata": {},
   "source": [
    "Wow, this is much better. By only setting the class weight to `balanced` and finding the best values of the hyperparameters, we were able to improve our model. \n",
    "\n",
    "If you remember, the negative class has fewer examples than the positive classes. You can see them in `support` in classification report. But our model is able to identify them correctly at 76%, and also is able to identify the positive examples at 97% without overfitting. That is precision. \n",
    "\n",
    "A few notes about Precison/Recall/F1 score:\n",
    "\n",
    "* Precision is the model accuracy on predicting positive examples correctly. \n",
    "* Recall is the ratio of the positive examples that are correctly identified by the model. \n",
    "\n",
    "* F1 score is the harmonic mean of precision and recall. \n",
    "\n",
    "The higher the precision and recall are, the higher the F1 score. But there is a tradeoff between them. Increasing precision will reduce recall, and vice versa. So it's fair to say that it depends on the problem you're trying to solve and the metrics you want to optimize for. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96d0b2f",
   "metadata": {},
   "source": [
    "### Evaluating the model on the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d18db2",
   "metadata": {},
   "source": [
    "It is only after we have improved the model that we can feed it to the test set. If we try to show the test set to the model while we are still training, we may cause a potential leak thus producing misleading predictions. \n",
    "\n",
    "Also, we will apply the same processing functions that we applied to the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "3e4f6abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_test = test_data.drop('binaryClass', axis=1)\n",
    "car_test_labels = test_data['binaryClass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "18686a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling the categorical features with the pipeline that we defined early\n",
    "# We don't fit on the test data. Only transform\n",
    "\n",
    "car_test_prepared = pipe.transform(car_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "4e320e33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle labels too\n",
    "\n",
    "car_test_labels_prepared = label_enc.transform(car_test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12a3457",
   "metadata": {},
   "source": [
    "Now we can make predictions on the test set. We will only have to call the metrics functions we created previously. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "b0ead3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8554913294797688"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(car_test_prepared, tree_best, car_test_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "8d5414f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53,  7],\n",
       "       [18, 95]])"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix(car_test_prepared, tree_best, car_test_labels_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "0c63170a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.88      0.81        60\n",
      "           1       0.93      0.84      0.88       113\n",
      "\n",
      "    accuracy                           0.86       173\n",
      "   macro avg       0.84      0.86      0.85       173\n",
      "weighted avg       0.87      0.86      0.86       173\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report(car_test_prepared, tree_best, car_test_labels_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7eda1f",
   "metadata": {},
   "source": [
    "The model never saw the test set but as you can see, the results are truly remarkable. The model can generalize well on the test set and that is because we improved it in the right ways. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e86c6d",
   "metadata": {},
   "source": [
    "This is the end of the notebook. We have learned how to build and how to regularize( or handle overfitting) the decion trees classifier. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17af5728",
   "metadata": {},
   "source": [
    "[Back to top](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "620a4d3e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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